Growing in popularity across the board
Cheap, accessible computing power and data resources combined with the support of big players like Microsoft, Google, Amazon and others have led to the rapid development of effective new machine-learning algorithms and groundbreaking innovation in the field of deep neural networks - known as 'deep learning'. Training deep networks takes huge quantities of processing power and data, which is now available to players large and small. Even more, most of these algorithms and innovative approaches are open source, simplifying reuse and adaptation.
All industries have their sights set on Al
While big players with specialized research labs have the resources needed to drive new developments, small and medium-sized companies are increasingly reliant on Al. Any business or organization that recognizes opportunities to improve processes, products and customer services should absolutely consider Al as a core technology for these evolutions.
To give just a few examples of success stories, Lee & Ally is a chatbot developed by legal firm deJuristen that provides legal advice. Swarovski created an intelligent visual search system to identify pieces of jewelry, and Rolls Royce uses predictive maintenance to reduce errors and cut fuel costs.
Al is being used in diverse applications
Driven by high-quality data and applied to mature processes, Al application areas include computer vision, natural language processing, forecasting, classification recommendation engines and clustering.
- Computer vision: the intelligent gleaning of information from images, such as quality, object detection and location, alphanumeric characters and similarities.
- Natural language processing: used to power smart chatbots - matching questions to commands - and automatic document tagging, among others.
- Forecasting: predicting numerical values to gauge, for example, product demand or consumer response to marketing campaigns.
- Classification: answers yes/no questions, e.g. "will this customer churn?" or "will this employee leave the business in the next year?"
- Recommendation engines: connecting the right products with the right customers at the right times.
- Clustering: grouping similar customers together to approach them in more personalized ways based on their actions, preferences or behaviors.